A new control method for microgrid power management

Solanki, A.; Nasiri, Adel; Bhavaraju, Vijay; Abdullah, Tuan Ab Rashid Tuan; Yu, David C.

doi:10.1109/icrera.2013.6749936

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…Various researches are going on the micro-grid power management issues. Several studies have been reported to balance the power in micro-grid in recent studies [13][14][15]. Power control and management in hybrid ac-dc micro-grid is discussed in [16].…”

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confidence: 99%

Power management of hybrid micro-grid system by a generic centralized supervisory control scheme

Ambia¹,

Al‐Durra²,

Muyeen³

2014

Sustainable Energy Technologies and Assessments

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Abstract-This paper presents a generic centralized supervisory control scheme for the power management of multiple power converters based hybrid micro-grid system. The system consists of wind generators, photovoltaic system, multiple parallel connected power converters, utility grid, ac and dc loads. Power management of the micro-grid is performed under two cases: grid mode and local mode. Central supervisory unit (CSU) generates command signal to ensure the power management during the two modes. In local mode, the dc loads in the ac-dc hybrid system can be controlled. In the case of grid mode operation, power flow between the utility grid and micro-grid is controlled. A novel feature of this paper is the incorporation of the multiple power converters. The generated command signal from the CSU can also control the operation of the multiple power converters in both grid and local modes. An additional feature is the incorporation of sodium sulfur battery energy storage system (NAS BESS) which is used to smooth the output power fluctuation of the wind farm.The effectiveness of the control scheme is also verified using real time load pattern. The simulation is performed in PSCAD/EMTDC.

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mentioning

confidence: 99%

Power management of hybrid micro-grid system by a generic centralized supervisory control scheme

Ambia¹,

Al‐Durra²,

Muyeen³

2014

Sustainable Energy Technologies and Assessments

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“…Thereby, to successfully integrate DERs units in the power system, many technical challenges must be overcome to ensure that high levels of efficiency and reliability will be fully harnessed. In this sense, some of the main challenges are related to [21], [22]:…”

Section: Hierarchical Controlmentioning

confidence: 99%

Control and energy management system of a microgrid using a genetic algorithm

Barrios¹

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A microgrid, as a controllable intelligent electric network, composed of distributed energy systems (DERs), energy storage systems (ESS) and controllable loads, require an Energy Management System (EMS) as a central entity responsible for coordinate control of DERs, for the dispatch of units under supply and demand uncertainty, for managing instantaneous active power balance, power flows and network voltage profiles, among others. Considering this central control structure, in this master dissertation it is proposed an Energy Management System composed of two optimization stages: a long-term and a short-term stage.The main function of the long-term stage is to solve the energy management problem considering an operational horizon of 24-hours to minimize simultaneously the operational cost and the power losses. To do this, it is used the Non-dominated Sorting Genetic Algorithm II (NSGA-II) complemented with a Quadratic Programing (QP) algorithm, to reduce the final complexity of the energy management problem. For the short-term stage, it is used a QP algorithm. The main function of the short-term stage is to guarantee power balance and reduce the impact of the forecast error in the operation of the distribution system.To develop the optimization algorithm MATLAB and GridLab-D are used to implement and simulate the EMS in a microgrid composed of a residential distribution network including batteries, renewable and fuel-based generation systems. To evaluate the developed EMS two main cases are studied, a perfect forecast case and real operational case. Finally, dynamic simulations are carried on in GridLabD to technically assess the impact of the optimal solution in the distribution system.

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